Testing of Hypotheses: A Practical Approach for Computing and Informatics

Hypothesis testing plays a crucial role in validating research claims in the domains of computing and informatics. It provides a systematic framework for data-driven decision-making. This chapter provides a comprehensive and accessible guide to hypothesis testing, specifically tailored for researchers and practitioners in computing and informatics. The chapter begins by introducing the fundamental concepts, including null and alternative hypotheses, significance levels, p-values, and types of statistical errors. It then presents both parametric and non-parametric tests commonly used in empirical computing research, such as t-tests, ANOVA, and the Chi-square test. Real-world case studies—ranging from A/B testing in web applications to algorithm benchmarking—are used to contextualise each technique. The chapter also highlights the integration of hypothesis testing in machine learning model evaluation and provides hands-on guidance using Python and R. By connecting theory with practice, this chapter equips researchers to apply hypothesis testing effectively in computing projects.